1. Predict Mobile App Popularity Mobile applications have truly revolutionized the way products and services are used. Businesses have started to realize the potential of having an app, and they have begun to capitalize on the app's user-friendly nature and easy-to- organize features. MobileStore is a leading online marketplace, where businesses can host their mobile apps and users can download them. In this competitive era, the more popular the app is, the higher the returns a business can expect. With this in mind, MobileStore wants to analyze what factors influence an app's popularity. Using machine learning, help them predict the popularity of an app uploaded to their marketplace. Explain how different features affect the decision. Files train.csv - data used for training along with target variable test.csv - data on which predictions are to be made format of Problem Perform an analysis of the given data to determine how different features are related to the app popularity. Build a machine learning model that can predict popularity For each record in the test set (test.csv), predict the value of the popularity variable (High or Low). Submit a CSV file with a header row plus each of the test entries, each on its own line. The file (submissions.csv) should have exactly 2 columns: app_id popularity (High or Low) Deliverables Well commented Jupyter notebook "submissions.csv" o S. Explore the data, make visualizations, and generate new features if required. Make 1. Predict Mobile App Popularity Mobile applications have truly revolutionized the way products and services are used. Businesses have started to realize the potential of having an app, and they have begun to capitalize on the app's user-friendly nature and easy-to- organize features. MobileStore is a leading online marketplace, where businesses can host their mobile apps and users can download them. In this competitive era, the more popular the app is, the higher the returns a business can expect. With this in mind, MobileStore wants to analyze what factors influence an app's popularity. Using machine learning, help them predict the popularity of an app uploaded to their marketplace. Explain how different features affect the decision. Files train.csv - data used for training along with target variable test.csv - data on which predictions are to be made format of Problem Perform an analysis of the given data to determine how different features are related to the app popularity. Build a machine learning model that can predict popularity For each record in the test set (test.csv), predict the value of the popularity variable (High or Low). Submit a CSV file with a header row plus each of the test entries, each on its own line. The file (submissions.csv) should have exactly 2 columns: app_id popularity (High or Low) Deliverables Well commented Jupyter notebook "submissions.csv" o S. Explore the data, make visualizations, and generate new features if required. Make